A system and a method for automatically identifying key dialogues in media is disclosed herein. In the method disclosed herein, the key dialogues engine receives the media asset and extract transcript data and supplementary data. The key dialogues engine processes the transcript data into a plurality of transcript data elements and associate the transcript data elements with respective data elements selected from the supplementary data. The key dialogues engine identifies one or more key dialogues from the associated transcript data elements based on configurable criteria, in operable communication with one or more of a plurality of data sources, wherein the configurable criteria comprises one or more of repetitive keywords, rhyming words, audio signal levels, matching keywords, text-based sentiments, dialogue similarity, repetitive dialogues, signature dialogues, entry dialogues recited by actors comprising protagonists and antagonists, faces of the actors, celebrity detection, image labels, and vector similarity scores.
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2. The system as claimed in claim 1, wherein the at least one processor is configured to identify one or more keywords that are repeated in individual and adjacent ones of the associated transcript data elements, in communication with a keyword database configured as one of the data sources, for identifying the one or more key dialogues from the associated transcript data elements based on the repetitive keywords.
This invention relates to a system for analyzing transcript data to identify key dialogues by detecting repeated keywords. The system processes transcript data elements, such as text from conversations or meetings, to extract meaningful segments. A processor identifies keywords that appear repeatedly in individual or adjacent transcript data elements, using a keyword database as a reference. The system then determines key dialogues based on these repetitive keywords, enabling efficient extraction of important information from large volumes of text. The keyword database serves as a data source to validate and refine the identified keywords, ensuring accuracy in dialogue selection. This approach enhances the ability to quickly locate and analyze significant discussions within transcript data, improving efficiency in tasks like meeting summarization, customer feedback analysis, or legal document review. The system automates the identification of recurring themes or topics, reducing manual effort and increasing reliability in extracting key insights from text-based interactions.
3. The system as claimed in claim 1, wherein the at least one processor is configured to identify one or more rhyming words having a similar phonetics scheme in individual and adjacent ones of the associated transcript data elements, in communication with a rhyming word database configured as one of the data sources, for identifying the one or more key dialogues from the associated transcript data elements based on the rhyming words.
This invention relates to a system for analyzing audio or video content to identify key dialogues based on rhyming patterns. The system addresses the challenge of automatically detecting meaningful or memorable segments in media by leveraging phonetic similarities in speech. The system includes at least one processor and a rhyming word database, which serves as a data source for phonetic matching. The processor analyzes transcript data derived from the audio or video content to identify words or phrases with similar phonetic schemes, particularly in adjacent or individual segments of the transcript. By cross-referencing these rhyming words with the database, the system determines key dialogues that may be significant due to their rhythmic or poetic structure. The rhyming word database contains phonetic information to facilitate accurate matching, ensuring that the identified dialogues meet specific linguistic criteria. This approach enhances content analysis by highlighting segments with rhythmic or lyrical qualities, which may be useful for applications such as media editing, content summarization, or creative writing assistance. The system automates the detection of rhyming patterns, reducing manual effort and improving efficiency in identifying impactful dialogue segments.
4. The system as claimed in claim 1, wherein the at least one processor is configured to extract keywords from each of the associated transcript data elements and compare the extracted keywords with a predetermined list of keywords to determine a count of the matching keywords in the each of the associated transcript data elements, and wherein the predetermined list of keywords is stored in a keyword database configured as one of the data sources, for identifying the one or more key dialogues from the associated transcript data elements based on the matching keywords.
This invention relates to a system for analyzing transcript data to identify key dialogues. The system addresses the challenge of efficiently extracting meaningful conversations from large volumes of transcript data, such as those generated in meetings, interviews, or customer interactions. The system processes transcript data elements, which may include text derived from audio or video recordings, and identifies key dialogues based on keyword matching. The system includes at least one processor configured to extract keywords from each transcript data element. These extracted keywords are then compared against a predetermined list of keywords stored in a keyword database, which serves as one of the system's data sources. The system counts the number of matching keywords in each transcript data element. The identified key dialogues are those transcript data elements that contain a sufficient number of matching keywords, as determined by the system's analysis. This approach allows for the automated identification of relevant discussions within transcript data, improving efficiency in data analysis and decision-making processes. The keyword database can be customized to reflect specific domains or topics of interest, ensuring the system's adaptability to different use cases.
5. The system as claimed in claim 1, wherein the at least one processor is configured to determine a similarity parameter defining a similarity between each of the associated transcript data elements and each of a plurality of dialogues, and wherein the plurality of dialogues is stored in a dialogues database configured as one of the data sources, for identifying the one or more key dialogues from the associated transcript data elements based on the dialogue similarity.
This invention relates to a system for analyzing transcript data to identify key dialogues. The system addresses the challenge of extracting meaningful conversations from large volumes of transcript data, such as those generated in customer service interactions, meetings, or other communication channels. The system includes at least one processor configured to process transcript data elements, which may include text, audio, or other forms of recorded communication. The processor is further configured to determine a similarity parameter that quantifies the similarity between each transcript data element and a plurality of predefined dialogues stored in a dialogues database. The dialogues database serves as one of the data sources for the system. By comparing the transcript data elements against the stored dialogues, the system identifies one or more key dialogues from the transcript data based on their similarity to the predefined dialogues. This allows for efficient retrieval of relevant conversations, enabling applications such as customer service analysis, meeting summarization, or automated response generation. The system may also include additional processors or modules to further refine the analysis, such as filtering or ranking the identified dialogues based on relevance or importance. The overall goal is to enhance the accuracy and efficiency of dialogue extraction from transcript data.
6. The system as claimed in claim 1, wherein the at least one processor is configured to identify one or more of repetitive dialogues and signature dialogues in the media asset by executing a probabilistic language model algorithm, for identifying the one or more key dialogues from the associated transcript data elements.
This invention relates to automated dialogue analysis in media assets, such as audio or video content, to identify repetitive or signature dialogues. The system addresses the challenge of efficiently detecting recurring or distinctive speech patterns within media content, which is useful for content analysis, editing, or metadata generation. The system processes a media asset by first extracting transcript data elements, which represent spoken content in the media. A probabilistic language model algorithm is then applied to analyze these transcript elements, identifying key dialogues that exhibit repetition or unique characteristics. The system distinguishes between repetitive dialogues, which occur multiple times within the media, and signature dialogues, which are distinctive or representative of the content. By automating this analysis, the system enables faster and more accurate identification of important or recurring speech segments, improving workflows in media production, archiving, and content management. The probabilistic language model leverages statistical techniques to assess the likelihood of dialogue patterns, ensuring reliable detection even in noisy or varied speech contexts. This approach enhances the efficiency of media processing tasks by reducing manual review efforts and providing structured insights into dialogue content.
7. The system as claimed in claim 1, wherein the at least one processor is configured to identify one or more entry dialogues recited by actors comprising one of protagonists and antagonists in the media asset, in communication with a face database and an actor information database configured as data sources, for identifying the one or more key dialogues from the associated transcript data elements.
The system analyzes media assets, such as films or television shows, to identify key dialogues spoken by actors. The system processes transcript data from the media asset to detect entry dialogues, which are specific lines spoken by protagonists or antagonists. These dialogues are identified by cross-referencing a face database and an actor information database, which serve as data sources to confirm the actors' roles and attributes. The system extracts and categorizes these dialogues based on their relevance to the narrative, such as character introductions or pivotal moments. The face database contains visual recognition data to match actors' appearances with their spoken lines, while the actor information database provides metadata like character roles and relationships. This analysis helps in summarizing or indexing media content for applications like automated content tagging, scene recognition, or audience engagement tools. The system enhances media analysis by linking spoken dialogue to actor identities and roles, improving the accuracy of dialogue extraction and contextual understanding.
8. The system as claimed in claim 1, wherein the at least one processor is configured to identify presence of one or more celebrities within time codes of the each of the associated transcript data elements, in communication with one or more of a face database and an actor information database configured as data sources, for identifying the one or more key dialogues from the associated transcript data elements based on the celebrity detection.
This invention relates to a system for analyzing video content to identify key dialogues, particularly those involving celebrities. The system processes video data to extract transcript data elements, which are segments of text derived from audio or speech recognition within the video. The system then analyzes these transcript data elements to detect the presence of one or more celebrities. This detection is performed by comparing visual or contextual data from the video against a face database and an actor information database. These databases serve as data sources to verify the identity of individuals appearing in the video. Once celebrities are identified, the system uses this information to determine key dialogues from the transcript data elements. The key dialogues are selected based on the detected presence of celebrities, ensuring that important or relevant portions of the video are highlighted. This system enhances video analysis by automating the identification of significant interactions involving well-known individuals, improving content indexing and retrieval for applications such as media archiving, entertainment, or marketing.
9. The system as claimed in claim 1, wherein the at least one processor is configured to determine a match in the image labels that are present within time codes of the each of the associated transcript data elements with a predetermined list of image labels, wherein the predetermined list of image labels is stored in an image labels database configured as one of the data sources, for identifying the one or more key dialogues from the associated transcript data elements based on the image labels.
This invention relates to a system for analyzing multimedia content, such as videos, to identify key dialogues based on image labels. The system addresses the challenge of automatically detecting and extracting meaningful segments from multimedia content by leveraging both visual and textual data. The system processes video content by extracting image labels from frames and associating them with corresponding time codes in the video. These image labels are then compared against a predefined list of image labels stored in a database. When a match is found between the extracted image labels and the predefined list, the system identifies the corresponding segments of the transcript data (e.g., subtitles or speech-to-text output) associated with those time codes as key dialogues. This approach ensures that the extracted dialogues are contextually relevant to the visual content, improving the accuracy of key dialogue identification. The system enhances multimedia analysis by integrating visual and textual data, enabling more precise and automated content extraction for applications such as video summarization, content indexing, or targeted advertising.
11. The method as claimed in claim 10, wherein the identification of the one or more key dialogues from the associated transcript data elements based on the repetitive keywords comprises identifying one or more keywords that are repeated in individual and adjacent ones of the associated transcript data elements, by the key dialogues engine in communication with a keyword database configured as one of the data sources.
This invention relates to a method for identifying key dialogues in transcript data, particularly in automated systems for analyzing conversations or interactions. The problem addressed is the need to efficiently extract meaningful and repetitive keywords from transcript data to identify significant dialogue segments, improving the accuracy and relevance of dialogue analysis. The method involves processing transcript data elements to detect repetitive keywords that appear in individual and adjacent transcript segments. A key dialogues engine interacts with a keyword database, which serves as a data source, to identify these repetitive keywords. The keyword database may contain predefined keywords or dynamically updated terms relevant to the context of the analysis. The engine analyzes the transcript data to find keywords that recur within or across adjacent transcript segments, indicating their importance or relevance to the conversation. By focusing on repetitive keywords, the method helps filter out less significant content and highlights key dialogue segments that may be critical for further analysis, such as customer feedback, technical support interactions, or other structured or unstructured conversations. The approach enhances the efficiency of dialogue processing by reducing noise and improving the extraction of actionable insights.
12. The method as claimed in claim 10, wherein the identification of the one or more key dialogues from the associated transcript data elements based on the rhyming words comprises identifying one or more rhyming words having a similar phonetics scheme in individual and adjacent ones of the associated transcript data elements, by the key dialogues engine in communication with a rhyming word database configured as one of the data sources.
Speech analysis and dialogue extraction. This invention addresses the problem of identifying significant dialogue segments within audio recordings. The method involves processing transcript data elements to identify key dialogues. This identification is achieved by a dialogue engine that analyzes the transcript data for rhyming words. Specifically, the engine looks for one or more rhyming words within individual and adjacent transcript data elements that share a similar phonetic scheme. This rhyming word detection is performed by the dialogue engine in conjunction with a rhyming word database, which is configured as a data source. The identified rhyming words serve as indicators for pinpointing the one or more key dialogues.
13. The method as claimed in claim 10, wherein the identification of the one or more key dialogues from the associated transcript data elements based on the matching keywords comprises extracting keywords from each of the associated transcript data elements and comparing the extracted keywords with a predetermined list of keywords by the key dialogues engine to determine a count of the matching keywords in the each of the associated transcript data elements, wherein the predetermined list of keywords is stored in a keyword database configured as one of the data sources.
A system and method for identifying key dialogues within associated transcript data. The problem addressed is the efficient and accurate extraction of significant conversational segments from large volumes of transcribed speech. This invention focuses on a process for identifying these key dialogues. The method involves processing associated transcript data elements. Keywords are extracted from each of these transcript data elements. These extracted keywords are then compared against a predefined, predetermined list of keywords associated with key dialogues. This comparison is performed by a key dialogues engine. The engine determines a count of matching keywords within each transcript data element. This predetermined list of keywords is maintained and accessed from a dedicated keyword database, which is configured as one of the data sources utilized by the system.
14. The method as claimed in claim 10, wherein the identification of the one or more key dialogues from the associated transcript data elements comprises identifying one or more entry dialogues recited by actors comprising one of protagonists and antagonists in the media asset, by the key dialogues engine in communication with a face database and an actor information database configured as data sources.
Audio-visual content processing and analysis. The technology addresses the challenge of automatically identifying significant dialogue within media assets. The method involves analyzing transcript data elements to pinpoint key dialogues. Specifically, this identification is achieved by recognizing entry dialogues spoken by actors who are designated as protagonists or antagonists within the media asset. This process is performed by a key dialogues engine that interacts with a face database and an actor information database, which serve as the data sources for this analysis.
15. The method as claimed in claim 10, wherein the identification of the one or more key dialogues from the associated transcript data elements based on the celebrity detection comprises identifying presence of one or more celebrities within time codes of the each of the associated transcript data elements, by the key dialogues engine in communication with one or more of a face database and an actor information database configured as data sources.
This invention relates to automated analysis of video content to identify key dialogues involving celebrities. The problem addressed is the difficulty in efficiently extracting meaningful segments from video content, particularly those featuring notable individuals, for applications like content summarization, indexing, or targeted advertising. The method involves processing video content to generate transcript data elements, each associated with time codes. A key dialogues engine analyzes these elements to detect the presence of celebrities by cross-referencing facial recognition data with a face database and actor information database. The engine identifies segments where celebrities appear, marking these as key dialogues based on their presence within the time codes of the transcript data elements. This allows for the extraction of segments where celebrities are speaking or featured, enhancing the relevance of the identified content. The system leverages databases containing facial recognition data and actor information to ensure accurate identification. The method improves upon prior approaches by automating the detection of celebrity involvement in video content, reducing manual effort and increasing precision in content analysis. This is particularly useful for applications requiring quick identification of high-value segments, such as media monitoring, content recommendation, or archival indexing.
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September 7, 2022
April 2, 2024
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